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The firm's forte is equity trading, but this role will encompass all of the products and strategies the fund trades, and will involve working with terabytes of structured and unstructured data. It will also afford a great deal of exposure to the numerous quant and trading heads across the business, with whom collaboration will be key.
Responsibilities will include designing and developing research methodologies, performing analysis using a variety of techniques (including time-series analysis), improving data mining and web scraping capabilities, building and enhancing mathematical models, and researching market microstructure. It will also entail cleaning and processing large datasets into cohesive information, and helping to present this information by creating unique visualisation tools.
Designing and developing research methodologies relating to data analysis, and analysing huge, often unstructured datasets, using a wide variety of techniques
Researching market microstructure, trading patterns, market quality, etc.
Building and enhancing mathematical models and research tools
Improving data mining and web scraping capabilities relating to market sentiment, impending changes to regulation, etc. – helping the business to stay abreast of anything that could affect trading patterns
1+ years of experience in a Quant Research / Data Science role at a financial institution
PhD from a top university in a highly quantitative subject (e.g. Applied Mathematics, Physics, etc.)
Proficiency in Python and C++, and the ability to learn new coding languages with ease
Experience acquiring, processing and analysing large quantities of unstructured data